Breaking the Iron Rice Bowl and Precautionary Savings: Evidence from Chinese State-Owned Enterprises Reform

Transcription

1 Breaking the Iron Rice Bowl and Precautionary Savings: Evidence from Chinese State-Owned Enterprises Reform Hui He, Feng Huang, Zheng Liu and Dongming Zhu

2 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS: EVIDENCE FROM CHINESE STATE-OWNED ENTERPRISES REFORM HUI HE, FENG HUANG, ZHENG LIU, AND DONGMING ZHU Abstract. We use China s large-scale reform of state-owned enterprises (SOEs) in the late 1990s as a natural experiment to identify and quantify the importance of precautionary saving for wealth accumulation. Before the reform, SOE workers enjoyed similar job security as government employees. Following the reform, over 27 million SOE workers were laid off, although government employees kept their iron rice bowl. The changes in unemployment risks for SOE workers relative to that of government employees before and after the reform provide a clean identification of changes in income uncertainty that help us estimate the importance of precautionary saving. In our estimation, we control for a self-selection bias in occupational choices and disentangle the effects of uncertainty from pessimistic outlooks. Our estimation suggests that precautionary savings account for about 30 percent of the wealth accumulation for SOE workers between 1995 and Date: November 30, Key words and phrases. Precautionary saving, uncertainty, structural change, self-selection bias, permanent income hypothesis, difference-in-difference methods. JEL classification: E21, P31, C20. He: Shanghai University of Finance and Economics; he.hui@mail.shufe.edu.cn. Huang: Shanghai University of Finance and Economics; huang.feng@mail.shufe.edu.cn. Liu: Federal Reserve Bank of San Francisco and SAIF; Zheng.Liu@sf.frb.org. Zhu: Shanghai University of Finance and Economics; zhu.dongming@mail.shufe.edu.cn. For helpful comments and suggestions, we are grateful to John Barron, Chris Carroll, Marcos Chamon, Zhao Chen, Russell Cooper, Frank A. Cowell, Hanming Fang, Jing Feng, Nicola Fuchs-Schündeln, Bart Hobjin, Mark Huggett, Selo Imrohoroglu, Dirk Krueger, Dan Lu, Kevin Mumford, David Slichter, Yong Wang, Shang-Jin Wei, Yi Wen, Dennis Yang, Motohiro Yogo, Xiaobo Zhang, Kai Zhao, Xiaodong Zhu and seminar participants at the Federal Reserve Bank of San Francisco, Fudan University, Georgetown University, IMF, the 2014 NBER Chinese Economy Meeting, the 2014 NBER Summer Institute EFACR Program Meeting, Purdue University, Southwest University of Finance and Economics, University of Pennsylvania, University of Rochester, 2013 Shanghai Macro Workshop, 2013 Econometric Society China Meeting, and the 1st Biennial Conference of China Development Studies. We thank Hanya Li for research assistance and Anita Todd for editorial assistance. Hui He acknowledges research support by Shanghai Pujiang Program, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and Key Laboratory of Mathematical Economics (SUFE), Ministry of Education. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco or the Federal Reserve System. 1

3 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 2 I. Introduction Precautionary savings are potentially important for wealth accumulation, especially for an emerging market economy like China that has experienced large structural changes associated with policy reforms, which may have led to substantial increases in economic uncertainty. However, estimating the importance of precautionary saving has been a challenge in the empirical literature. One difficulty is the identification of large and exogenous variations of income uncertainty (Lusardi, 1998; Carroll and Kimball, 2008). Some empirical studies use cross-sectional variances of income as a proxy for income uncertainty (Carroll and Samwick, 1998). But this proxy is not ideal because of measurement errors and potential endogeneity bias (Kennickell and Lusardi, 2005). A second difficulty for estimating the importance of precautionary saving stems from a self-selection bias in occupational choices. Precautionary saving depends not just on risk, but also on risk preferences (Caballero, 1990, 1991). A more risk averse individual is likely to choose a job with lower income risks, and a less risk averse worker is more likely to choose an occupation with higher income risks (with potentially higher expected income). Failing to control for self-selection in occupational choices may lead to a significant downward bias in estimating the importance of precautionary saving (Fuchs-Schündeln and Schündeln, 2005). Partly reflecting these difficulties in measuring income uncertainty and correcting selfselection biases, the existing literature obtains mixed evidence of precautionary saving. For example, some studies report weak or no evidence of precautionary saving (Dynan, 1993; Guiso et al., 1992), while some other studies attribute a large fraction (50% or more) of household wealth accumulation to precautionary saving (Carroll and Samwick, 1998; Gourinchas and Parker, 2002). In this paper, we present a new empirical approach to identifying and quantifying precautionary saving. We use the large-scale reforms of state-owned enterprises (SOEs) in China in the late 1990s as a natural experiment to identify variations in income uncertainty. Prior to the reform, workers in SOEs and in the government sector (GOV) had similar job security, including guaranteed pensions and near-free health care and housing. In this sense, workers in both sectors held an iron rice bowl before the reform. Following the reform, however, over 27 million workers in the SOEs were laid off between 1998 and Those workers lost not just their jobs, but also the associated benefits. In contrast, workers in the government sector where few layoffs occurred were little affected by the reform; they were able to hold on to their iron rice bowl. The massive layoffs in the SOE sector significantly changed the perceived job security for the remaining SOE workers. The reform was largely unexpected to 1 Source: China Labor Statistical Yearbook 2003.

4 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 3 an individual worker and it created significant variations of unemployment risks for workers across the SOE and GOV sectors. Thus, the reform provides a clean identification of relative income risks stemming from perceived job uncertainty. To estimate the importance of precautionary saving, we use data from the Chinese Household Income Project (CHIP) survey. We focus on the years 1995 and The large-scale SOE reform started to have significant impacts on SOE employment in 1997, with the effects gradually phasing out by Our sample thus covers both the pre- and post-reform periods. To identify and quantify the contribution of precautionary saving to wealth accumulation, we exploit the differences in saving behavior both across sectors (SOE vs. GOV) and across time (before and after the SOE reform) a difference-in-differences (DID) approach. The time variations (between 1995 and 2002) of the relative saving behavior of workers across the two sectors capture the magnitude of precautionary savings caused by the SOE reform. To appropriately quantify the importance of precautionary saving, we also need to control for self-selection biases that may arise from correlations between occupational choices and individual risk attitude. As shown by Fuchs-Schündeln and Schündeln (2005), failing to control for self selection would lead to a significantly underestimated magnitude of precautionary savings. However, removing the bias caused by self section has been an important challenge for empirical studies of precautionary saving (Fuchs-Schündeln and Schündeln, 2005). To mitigate the self-selection bias in our estimation, we explore the micro-details of the CHIP survey data. The surveys in both 1995 and 2002 contain a question about how a worker obtained her current job. Some workers find jobs through a search and matching process; but in our sample, a majority of workers (over 70 percent) have jobs assigned by the government. For assigned jobs, the government has the final power to determine the worker s occupation and compensation. Indeed, focusing on jobs assigned by the government in our sample turns out to substantially weaken the link between workers occupational choices and their risk attitude. 3 The SOE reform might affect not only the perceptions of future income uncertainty, but also the expected future income growth rate. For example, after witnessing the impact of the reform on the relative job security, an SOE worker might expect not only an increase in future income risks but also a decline in future income levels. Declines in expected income 2 We also have the CHIP survey data for 1988 and 2007, although those surveys do not report wealth information and are thus less useful for studying precautionary savings. 3 In practice, job assignments by the government were not completely independent of worker preferences because workers could signal their preferred job positions to the government before actual assignments took place. By focusing on the subsample with government assigned jobs, we are able to mitigate, but not completely eliminate the effects of self-selection. saving that are substantially greater than that obtained from the full sample. Nonetheless, we still obtain estimates of precautionary

5 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 4 would also raise current saving. Such saving behavior, however, is driven by the worker s desire for intertemporal consumption smoothing (i.e., an effect related to the permanentincome hypothesis, or PIH), not by precautionary motives. To disentangle the effects of precautionary motives on saving from the PIH effects, we use a unique question in the 2002 CHIP survey that asks households about their expectations of income paths in the next five years. We restrict our sample to include only those workers who do not expect income to decline. This approach enables us to mitigate the PIH effects that could cause an upward bias in the estimation of precautionary saving. By identifying job uncertainty caused by the SOE reform, mitigating self-selection bias in occupational choices, and controlling for PIH effects, we obtain estimates of precautionary savings that are significant both statistically and economically. We estimate that precautionary savings accounted for about 30 percent of total financial wealth accumulations for urban SOE workers during the period from 1995 to Our work is closely related to the important contribution by Fuchs-Schündeln and Schündeln (2005), who use the event of German reunification to identify and quantify potential biases for estimating precautionary savings caused by self-selection into occupations according to risk preferences. Fuchs-Schündeln and Schündeln (2005) first use the German Socio-Economic Panel (GSOEP) survey data in the post-reunification period from 1998 to 2000 to test the existence of precautionary savings by examining whether wealth holdings by civil servants, who face lower labor income risks, were significantly lower than those by households working in other occupations. They then show that, in the sample of households whose occupations were determined in the former German Democratic Republic (GDR) before the reunification, self-selection is not important because labor income risks were essentially absent for any occupations and occupational choices in the former GDR were often restricted by political considerations. After the reunification, many individuals working in the civil services of the former GDR were granted the status of civil servants. Thus, for the sample of households whose occupations were determined in the former GDR, the difference between the wealth holdings of civil servants and those of workers in other occupations captures precautionary savings in the absence of self-selection. Finally, they measure the self-selection bias by comparing the magnitude of precautionary savings estimated from the sample of former GDR households (whose occupations were determined before the reunification) with that obtained in the sample of West German households. They find that the self-selection bias is quantitatively important. Our approach to controlling for self-selection biases shares a similar spirit with the approach adopted by Fuchs-Schündeln and Schündeln (2005). We restrict our sample to the households whose jobs were assigned by the Chinese government. Similar to the case of the

6 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 5 former GDR, job assignments by the government in China were often restricted by political considerations and job outcomes were often unrelated to individual preferences. Further, we apply our sample restrictions to government-assigned jobs for both the pre- and postreform periods. We show that these restrictions substantially mitigate the bias caused by self-selection of risk-averse individuals into low-risk government jobs after the reform. More importantly, our unique dataset from the CHIP surveys allows us to explicitly identify changes in labor income risks and, in particular, unemployment risks for government employees relative to those for SOE workers caused by the large-scale SOE reform. Thus, we are able to examine the consequences of such changes in labor income risks for wealth holdings by these two groups of workers. Before the reform, SOE workers had similar job security as government employees. Accordingly, we find that the wealth holdings are not significantly different between the two groups of workers. After the reform, massive layoffs in the SOE sector substantially raised unemployment risks for SOE workers, but not for government employees. Accordingly, we find that the wealth holdings by SOE workers were significantly higher than those by government employees. By examining the changes in relative wealth holdings between the two groups of workers caused by the changes in relative unemployment risks, we are able to provide a clean identification of precautionary saving. To our knowledge, our work is the first study in the literature to identify precautionary saving by using a natural experiment with exogenous variations of income risks both across sectors and across time. 4 II. Related Literature Our focus is on the general issue of estimating precautionary saving using China s data. Although we do not intend to directly address the specific issue of the sources of the rising Chinese saving rate, there is a growing literature that attempts to quantify the importance of precautionary saving for explaining China s rising saving rate. Earlier studies obtain mixed evidence that supports the precautionary-saving view (Kraay, 2000; Gourinchas and Parker, 2002; Carroll et al., 2003; Meng, 2003). Carroll and Kimball (2008) provide a useful survey of this literature. A more recent study by Chamon and Prasad (2010) uses the annual Chinese Urban Household Survey from 1995 to 2005 to disentangle different motives behind the rising urban household saving rate. Chamon and Prasad (2010) find that the increases in private burden of 4 In the GSOEP data used by Fuchs-Schündeln and Schündeln (2005), the sample of GDR households begins in 1990, after the reunification. Thus, one cannot use that dataset to examine changes in relative wealth holdings by former GDR households caused by changes in their relative labor income risks following the German reunification event.

7 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 6 education, health and housing expenditures seem among the strongest candidates for explaining the increases in saving rates. Chamon et al. (2013) document a sharp increase in income uncertainty associated with increases in transitory idiosyncratic shocks among Chinese urban households. They argue that rising income uncertainty induces younger households to raise their saving rate significantly. An alternative explanation for the rising Chinese saving rate is provided by Wei and Zhang (2011), who present evidence that sex imbalances caused by China s one-child policy have induced a competitive savings motive: with a shortage of girls, parents with a son save more to increase the relative attractiveness of their son in a tighter marriage market. They show that this competitive savings motive is empirically important. Complimentary to this literature, we provide empirical evidence that increases in economic uncertainty associated with large structural changes in China have contributed to substantial precautionary wealth accumulation. III. Some Background of the SOE Reform From 1949 to 1978, China s economy was under a central-planning regime. The government maintained tight controls over production and factor allocations. Most jobs were assigned by the government. To support the goal of industrialization, workers were paid subsistence wages and, in exchange, they were guaranteed life-time employment along with near-free housing, education, health care, and pension (Cai et al., 2008). This cradle-to-grave regime is known as the iron rice bowl, which has long been advocated as one advantage of China s socialist system. The open door economic policy and nationwide reform initiated by Deng Xiaoping in the late 1970s initiated China s transition to a free-market economy. In the mid-1980s, the government introduced a labor contract system, under which workers were permitted to search for jobs and employers gained some flexibility in hiring (Meng, 2000). These reform policies led to a large-scale urban migration and increased competition facing SOEs. Following Deng Xiaoping s tour of the south in 1992, more liberalization policies were adopted by the government, leading to a boom in urban economies, which further intensified competition for SOEs. At that time, with soft budget constraints and the requirement to implement the government s goal of full-employment, the SOE sector had substantial redundant labor and many SOE firms were making losses. In 1995 and 1996, around 50% of the SOEs (mostly small or medium sized) reported losses (Meng, 2003). The Asian financial crisis in 1997 exacerbated the situation. The Chinese government was forced to take actions to improve efficiency of the SOEs and to stem losses. Specific actions were laid out at the Fifteenth Communist Party Congress

8 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 7 held in September A central spirit of the restructuring policy was to grasp the large and let go of the small. Large (and usually more profitable) SOEs in strategic sectors such as electricity, oil, raw materials, and telecommunications were corporatized and maintained under state controls, while smaller (and often loss-making) SOEs were either privatized or let go bankrupt (see Hsieh and Song (2013)). These policy changes led to a massive layoff (xia gang in Chinese) of SOE workers starting in 1997, the scale of which was unprecedented. By the end of 1997, a cumulative of about 6.92 million SOE workers were laid off. The wave of layoffs reached a peak in 1999, with about 6.2 million SOEs workers losing their jobs in that year. The massive wave of layoffs started to subside by During the 5-year period from 1997 to 2002, a remarkable total of over 27 million SOE workers had been laid off. 5 There is evidence that the SOE layoffs were concentrated in small and loss-making firms and in some demographic groups. For example, female, less educated, less skilled, less healthy workers, and non-members of the communist party were more likely to be laid off than others. Workers in SOEs owned by local governments were also more likely to be laid off than those in SOEs owned by the central government (Appleton et al., 2002). However, the scale and the breadth of the layoffs were largely unexpected by individual workers (see Appendix A for a case study of the SOE layoff experience). Thus, for the SOEs workers who were fortunate to keep their jobs, the reform that broke the iron rice bowl had led to significant changes in their perceptions about future job security and substantially increased their perceived income uncertainty. IV. Data and Empirical Strategy IV.1. Data. The data that we use are taken from Chinese Household Income Project (CHIP) surveys. Those surveys were conducted by the Chinese Academy of Social Science (CASS) and National Bureau of Statistics (NBS) through a series of questionnaire-based interviews done in rural and urban areas in China in 1988, 1995, 2002 and The households in each survey are randomly selected following a strict sampling process so that they are nationally representative. The surveys cover a sample of about 15,000 to 20,000 households in about 10 provinces in China. The surveys contain detailed data on rural and urban households economic status, employment, levels of education, sources of income, household compositions, household expenditures and wealth. The CHIP data have been frequently used in the empirical literature (e.g., Wei and Zhang 2011). 5 Data source: China Labor Statistical Yearbook, 2003.

9 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 8 In this paper, we focus on the sample of urban households in the CHIP surveys of 1995 and These two years span the period of China s large-scale SOE reforms that had led to massive layoffs in the SOEs. As we described in Section III, before the reform, workers in SOEs had similar life-long employment status as those in GOV sectors; in both sectors, workers faced little unemployment risks and income uncertainty. However, since the reform started in 1997, a large number of SOE workers were laid off while GOV workers were able to keep their iron rice bowl. The reform thus injected substantial unemployment risks to SOE workers relative to GOV workers. The different impact of the reform on workers across the two sectors provides an ideal natural experiment for us to identify precautionary saving due to a sudden and substantial increase in unemployment risks. To estimate the quantitative importance of precautionary saving, we exploit changes in saving behaviors associated with the SOE reform between SOE workers and GOV workers. Thus, we restrict our sample to include only those households whose heads work in either the SOE sector or the GOV sector. The SOE sector includes firms that are directly owned by the government (including central, provincial, and local governments), those in which the government holds a controlling share of stocks, and those under collective ownership. The GOV sector includes all levels of government and public institutions. 7 We further restrict our sample to include prime-age workers, whose ages are between 25 and 55 years. This choice is partly driven by concerns of measurement errors in wealth and permanent income for younger workers. It is also driven by concerns that the saving behaviors of workers close to retirement ages change dramatically for reasons more closely related to life-cycle factor than to income uncertainty (Carroll and Samwick, 1998; Gourinchas and Parker, 2002). 8 With these sample restrictions, we end up with 4390 household-level observations in 1995, consisting of 2977 SOE workers and 1413 GOV employees; and in 2002, we have 3027 observations consisting of 1702 SOE workers and 1325 GOV employees. IV.2. Construction of Variables. In this subsection, we discuss the construction of all variables used in our empirical studies. Table 1 provides a brief description of these variables; Table 2 reports summary statistics of the full sample; and Table 3 compares some key characteristics between GOV and SOE workers. 6 The surveys in 1988 and 2007 do not provide sufficient wealth information, so we cannot use them to study precautionary saving. 7 According to the China Labor Statistics Year Book, the SOE and the GOV sectors together employed about 94.1% of total urban workers in This share declined to 75.5% in During this period, however, the large-scale SOE reform has led to a substantial decline in the relative share of employment in the SOE sector from 70.5% to 42.4%. 8 The normal retirement age for female workers in China is between 50 and 55; for male workers, it is between 55 and 60.

10 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 9 To estimate the quantitative importance of precautionary saving, we focus on the empirical relation between a measure of savings and a measure of income uncertainty, while controlling for a few demographic characteristics. We measure household saving behavior by the ratio of financial wealth to permanent income (i.e., the W/P ratio; see Table 1 for a description of these variables). 9 We use the stock of financial wealth reported in the surveys instead of the flow of saving or the saving rate to measure an individual s saving behavior for two reasons. First, unlike the flow of saving, financial wealth is not influenced by high-frequency fluctuations in income and expenditures. Thus, it is better able to capture long-run (or average) saving behavior in which we are interested. Second, financial wealth is a direct measure of cumulative savings and is thus less subject to measurement errors than the flow of saving or the saving rate, which are indirectly calculated based on income and consumption expenditures. Moreover, our measure of financial wealth includes mostly liquid assets, which are relevant for studying precautionary saving (Carroll and Samwick, 1998). We normalize financial wealth by permanent income to obtain a measure of average savings. We thus need to construct a measure of permanent income. In the CHIP datasets, survey participants report incomes earned by household heads during the current year and the recent past. In the 1995 survey, we observe household head earnings from 1990 to 1995; in the 2002 survey, we observe earnings from 1998 to We use this information to construct a measure of permanent income following a similar approach used by Fuchs-Schündeln and Schündeln (2005). This is done in three steps. First, we calculate a household head s earnings relative to the average earnings of all households in each year with reported earnings. Second, we take the time-series average of the household relative earnings. Third, we multiply the household head s income in each of the survey years (1995 or 2002) by the average relative earnings to obtain an annual permanent income for the household in that year. 11 To mitigate potential measurement errors introduced in the process of constructing permanent income, we follow Fuchs-Schündeln and Schündeln (2005) by instrumenting permanent 9 We deflate all nominal variables in the sample by the urban household consumer price index (CPI), with 2002 as the base year. 10 For a single-earner family, the household head is the bread winner. For a multiple-earner family, the head is the person with the highest income. 11 We use box plot to detect possible outliers in the data of wealth measures and permanent income. We first determine the first and third quartiles (denoted by Q 1 and Q 3, respectively) for the data set. Define the interquartile range IQR = Q 3 Q 1, which is a measure of noise or scale for the data set. Observations that are more than three IQR s are treated as potential outliers and excluded from the sample.

11 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 10 income using education dummies and interactions of education with age and age-squared as instruments in all the regressions. We consider two types of income uncertainty. The first relates to the cross-sectional variance of the log income used in the literature (Carroll and Samwick, 1998). Since the average household income in our sample has grown from 1995 to 2002 and different demographic groups might have experienced different growth, directly using the cross-sectional variance of income would be inappropriate, especially for making cross-group comparisons. We thus use a unit-free measure, which is the coefficient of variation (CV) of log income, defined as the ratio of the standard deviation to the mean of log household head s income over the past six (or five) years as reported in the 1995 (or 2002) CHIP surveys. The second type of income uncertainty that we consider captures the uncertainty stemming from unemployment risks specific to SOE workers. In particular, we include in the regression an SOE dummy variable, which takes a value of one if the household head works in the SOE sector and zero if the household head works in the GOV sector. If our hypothesis is correct, then we should expect SOE workers to increase their savings relative to GOV workers after the reform took place. We use this source of income uncertainty associated with the largescale SOE reform as the key to identifying precautionary saving. In our estimation, we control for the effects of a number of demographic characteristics of households, including the household head s age, age-squared, gender, marital status, education, occupation, the household size, status of children (the ages of children, the number of boys, and the number of children at school), health care (public health care, public health insurance, or own payments), home ownership status, and others. Table 2 shows some details of these demographic variables. We categorize the education level of a household head into four groups: elementary school and below, junior middle school, senior middle school, and post-secondary (college). We take the first group as our reference group and construct four education dummies. We also divide the occupations of the household heads into five groups: professional, director or manager, skilled or office workers, unskilled or service workers, and others. The group of others is our reference group in the regressions. The health care reform enacted in 1998 significantly changed the share of household expenditures on health care. We categorize the types of health care that the households receive into three groups: public health care (almost free), public health insurance, and self-financing of health care. As shown in Table 2, in 1995, 71.3% of households in our sample had access to free public health care. This share was halved to about 35.0% in 2002, reflecting the impact of the health care reform on household health expenditures.

12 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 11 To control for the effects of rising education expenditure on households saving rate, we include in the regressions the mean age of children and the number of children at school. To control for effects of potential competitive savings motive emphasized in Wei and Zhang (2011), we add the number of boys among children as an independent variable. Purchasing a house is argued to be one of the major motives of saving for Chinese households (Wei and Zhang, 2011). The housing reform that started in 1998 has led to extensively privatized housing market. As shown in Table 2, the homeownership rate in our sample doubled over the seven year period, from 42.0% in 1995 to 80.4% in We control for the potential effects of saving for home purchases by including a non-homeownership dummy that takes a value of one if the household is not a home owner and zero otherwise. We also include in our regressions an interaction term between the SOE dummy and non-homeownership to control for the effects of potential savings by SOE workers for home purchases rather than for precaution against future unemployment risks. Since the SOE reform and the massive layoffs hit some industries and geographic areas more heavily than others, we include in our regression two dummy variables that indicate the industries and provinces where the household head worked. As revealed by Table 3, the reform has impacted GOV workers and SOE workers differently. In 1995, before the reform took place, GOV employees had on average modestly more financial wealth and higher permanent income than SOE workers. They were also more likely to own houses than the SOE workers. Nearly 90% of the GOV jobs were assigned by the government, while 80% of the SOE jobs were assigned by the government. In 2002, after the reform, the wealth and income gaps between the two sectors widened. The homeownership rate rose sharply for both groups (from 45% to 83% for GOV workers and from 40% to 78% for SOE workers). The reform also led to different perceptions of future income across the two groups. In the 2002 survey, about 24% of the SOE workers expected to have declines in income in the next five years, while just a bit over 11% of GOV employees expected income to decline. As we discuss below, pessimistic income outlooks can also raise saving, but such saving behavior represents a desire for intertemporal consumption smoothing (or PIH effects) rather than a motive for precautionary saving. To obtain a clean estimation of precautionary saving, we need to disentangle the PIH effects from the precautionary motive. IV.3. Empirical Strategies. Following Lusardi (1998) and Carroll et al. (2003), we estimate the importance of precautionary saving using the ratio of financial wealth to permanent income as the dependent variable. The estimation equation for each household i is given by W i /P i = β 0 + β 1 SOE i + β 2 RISK i + β 3 log(p i ) + β 4Z i + v i, (1)

13 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 12 where W i /P i is the ratio of financial wealth (W i ) to permanent income (P i ), SOE i is a dummy variable, which takes a value of one if the household head works for an SOE and zero if the household head works for a government or public institution (GOV), RISK i denotes individual income risks measured by the coefficient of variations (CV) of log annual income of the household head for the past 5 or 6 years, Z i is a vector of demographic characteristics, and v i is the error term. We choose financial wealth/permanent income (W/P ) ratio as the dependent variable for three reasons. First, precautionary saving model predicts that W/P ratio should be a function of age and other household characteristics (Lusardi, 1998; Carroll and Samwick, 1998), and therefore using W/P ratio as the dependent variable is consistent with the theoretical framework. Second, as a normalized measure of wealth, W/P ratio makes wealth of households with different income levels comparable. Third, W/P ratio captures a household s cumulative savings, which helps to establish a natural link between wealth accumulation and saving behavior. As we argue above, the SOE reform in the late 1990s mainly affected the job security for SOE workers, but not for GOV workers. Thus, the reform provides a natural experiment that helps identify changes in precautionary savings for SOE workers relative to GOV workers. This aspect of the data allows us to use a difference-in-difference (DiD) approach to identify changes in the relative precautionary savings across the two sectors before and after the reform. To implement this idea, we estimate the regression equation (1) using the CHIP survey data for each of the two years in our sample, one before the reform (1995) and the other after (2002). The estimated coefficient (β 1 ) of the SOE dummy variable from each regression captures the excess savings by SOE workers relative to GOV workers. All else equal, changes in the estimated value of β 1 from 1995 to 2002 captures changes in the relative magnitude of precautionary savings of the SOE workers caused by increases in their unemployment risks following the breaking of the iron rice bowl. 12 To estimate the quantitative importance of precautionary saving, it is necessary to correct a self-selection bias in occupational choices. An individual with high risk aversion has an incentive to choose a job with low income risk. Similarly, a worker with low risk aversion may want to choose a job with high income risk (with potentially high expected income). 12 Our approach is slightly different from the standard DiD approach, which pools data in all sample years and thus puts an implicit restriction that the coefficients on all variables but the SOE dummy should be identical across time. With our approach, we estimate a separate regression for each of the two sample years and thus we do not impose such restrictions. Since China has gone through large structural changes between 1995 and 2002, many demographic aspects of our sample are likely to have changed during that period. Thus, taking a more flexible DiD approach as we do here is appropriate.

14 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 13 Failing to control for self-selection in occupational choice may lead to significant downward bias in estimating the importance of precautionary savings (Fuchs-Schündeln and Schündeln, 2005). We address the self-selection issue by restricting our sample to include only workers whose jobs were assigned by the government. For assigned jobs, the government has the final power to determine the worker s occupation and compensation; and thus, the worker s occupational choice is likely unrelated to her preferences. The CHIP survey contains a question that asks an interviewee whether his current job was assigned by the government or found through a searching and matching process such as passing an exam, responding to a vacancy post, or referred by friends. We use answers to this question to identify the subsample of workers with government-assigned jobs. As shown in Table 2, 82.6% of workers had jobs assigned by the government in 1995 and this fraction declined somewhat to 71.7% in With this restriction imposed, we obtain a modestly smaller sample of 3627 in 1995 (with 2369 SOE workers and 1258 GOV employees) and 2170 in 2002 (with 1171 SOE workers and 999 GOV employees). It is important to recognize that, while the SOE dummy (SOE i ) in the regression equation (1) captures income uncertainty specific to SOE workers, the RISK i variable reflects idiosyncratic income risks for all workers. These two variables are indeed uncorrelated in our sample, with a correlation coefficient of about 0.04 in each of the two sample years, consistent with our view that they capture different aspects of the risks for individual households. In estimating the model, we also need to address the issue that arises with observations of zero wealth. In our sample, 11.3% of households have zero wealth in 1995 and this share declined to 4.5% in We treat this issue as a censored data problem. We address the issue by estimating an instrumental variable Tobit regression (IV-Tobit). In a robustness check, we also estimate the baseline model in equation (1) by eliminating the zero-wealth observations from our sample and then applying the standard two-stage least squares (2SLS) method (see Section VI.4). V. Empirical Results We now discuss the main empirical results and provide evidence of precautionary saving. We first discuss the estimation results with self-selection corrected in Section V.1. We then examine the quantitative importance of the self-selection bias in Section V.2. Finally, we discuss our approach to disentangling the permanent income effects from precautionary saving in Section V.3. V.1. Evidence of precautionary saving. We now present evidence of precautionary saving when we correct the self-selection bias by focusing on the subsample with government

15 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 14 assigned jobs. The estimation results for 1995 and 2002 are shown in Table 4 (columns (i) and (iii)). The parameter of interest is the coefficient of the SOE dummy, β 1, which captures the difference in wealth accumulation between SOE and GOV workers when we control for the effects of all the demographic characteristics in the empirical model described by equation (1). The estimated value of β 1 is statistically insignificant in 1995 (column (i)), indicating that wealth accumulations of SOE workers and GOV workers were similar in By 2002, however, SOE workers had accumulated significantly more financial wealth than GOV employees (reflected by a much large estimate of β 1, see column (iii)). This evidence suggests that the relative saving behaviors of SOE workers has changed during that period. In particular, the difference between the two estimated values of β 1 is large ( = 0.633) and statistically significant, with a p-value of The substantial increase in β 1 reflects the effects of the large-scale SOE reform on workers unemployment risks and thus captures the importance of precautionary saving. We now discuss the interpretations of estimated coefficients for the control variables. In addition to the demographic controls such as the age, gender and occupation of the household head, we highlight here a few important control variables. These controls include an indicator of idiosyncratic income risks (CV), the permanent income (P) that captures non-homothetic preferences, and additional income or expenditure risks introduced by reforms between 1995 and 2002, such as health care reforms, education reforms, and housing reforms. We continue to focus on the case with self-selection bias controlled for (columns (i) and (iii) in Table 4). The estimated coefficient β 2 of idiosyncratic income risks (CV) is positive and significant at the 1% level for both years. The estimated coefficient β 3 of log(p ) is positive, but it is significant only in 2002, implying a significant income effect for that year. To control for the impact of health care spending on households saving behavior, we include in the regression a dummy variable indicating public health care (mostly free) and another dummy indicating public health insurance. The coefficients of both dummy variables are small and insignificant in 1995 but become significantly negative in This result is intuitive. In 1995, most workers were covered under a near-free public health care system, so that the health care status did not impose any significant impact on households saving behavior. However, after the health care reform that started in 1998, a significant fraction of health care spending was shifted to private households. Thus, households not covered by public health care or public health insurance had a strong incentive to save. This finding is consistent with that obtained by Chamon and Prasad (2010), who report that declining public provisions of health care in the late 1990s in China created strong motives for precautionary saving against potential health expenditure shocks.

16 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 15 To control for the effects of education reforms on households saving behavior and potential competitive saving motive in the marriage market emphasized by Wei and Zhang (2011), we include in our regression three additional variables: the mean age of children, the number of children enrolled in schools, and the number of boys in each household. Our estimation shows that the mean age of children does not explain wealth accumulation. The number of children enrolled in schools tends to reduce wealth accumulation in both years, although the effects were significant only in Having more children at school requires more expenditure on education after the education reforms in the late 1990s, which led to lower disposable income and reduced wealth accumulation. The number of boys contributes positively to savings in 1995, consistent with the findings in Wei and Zhang (2011), although the estimated coefficient is insignificant for that year. In 2002, however, having more boys in the household actually reduced savings and the effect is significant at the 10% confidence level. A possible explanation lies in the reforms of social security and the pension system, which substantially weakened the public safety net for retirees. In the Chinese culture, sons are supposed to take responsibility of taking care their elderly parents. Therefore, facing an uncertain future of safety net, having more boys means having better insurance for their parents. Parents thus do not need to save that much for their old-age consumption. In our 2002 sample, this self-insurance effect of having more boys dominates the potential competitive savings motive highlighted by Wei and Zhang (2011). Finally, to control for the effects of housing reform on saving, we include in the regression a non-homeownership dummy and an interaction term between a non-homeowner dummy and the SOE dummy. The coefficients for these two variables are not significant for both years. A possible explanation is that, in 2002, the housing market was not fully developed and home purchases were still heavily subsidized. This result indicates that the saving motive for home purchases was weak in both 1995 and V.2. The self-selection bias. The literature shows that self-selection in occupational choices can lead to a substantial downward bias in the estimated magnitude of precautionary saving (Fuchs-Schündeln and Schündeln, 2005). An individual with high risk aversion has an incentive to choose a job with low income risk. Similarly, a worker with low risk aversion may want to choose a job with high income risk (with potentially high expected income). To correct the downward bias caused by self-selection, we restrict our sample to workers whose jobs were assigned by the government. To the extent that the government s job assignments are not systematically correlated with individual risk attitude, our sample restriction should mitigate the bias caused by self-selection in occupational choices. Our estimation shows that the downward bias caused by self-selection was not statistically significant in 1995, but it became significant in This can be seen by comparing the

17 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 16 estimated value of β 1 from the subsample with government-assigned jobs to the estimate obtained from the full sample (i.e., the difference between β 1 in columns (i) and (ii) for 1995 and in columns (iii) and (iv) for 2002). In 1995, self-selection did not cause a significant downward bias in the estimated value of β 1 (the magnitude of the bias is = 0.051). In 2002, self-selection bias became statistically significant, with a magnitude of about = 0.396, which is equivalent to a little under 5 months of permanent income. 13 Thus, without correcting the self-selection bias, we would have substantially underestimated the importance of precautionary saving, especially for the post-reform period in V.3. Disentangling PIH Effects from Precautionary Saving. The large-scale SOE reform not only led to significant changes in the relative job security between GOV and SOE workers, they might also produce potentially large differences in future income expectations between the two groups. All else equal, a worker who expects declines in future income would like to increase saving, but such increases in saving reflects a desire for intertemporal consumption smoothing (i.e., a permanent income effect) rather than a motive of precautionary saving. To the extent that the difference in perceived job security and income expectations between the two groups of workers were both caused by the SOE reform, disentangling the PIH effect from precautionary saving is particularly important for the post-reform period in To isolate the effects of precautionary motives on saving from the PIH effects, we use a unique question in the 2002 CHIP survey that asks households about their expectations of income paths for the next five years. As Table 3 shows, a significant fraction of SOE workers surveyed in 2002 expected future income declines (23.8%), although a much smaller fraction of GOV workers expected income declines (11.4%). Thus, the reform has caused different income expectations in addition to different unemployment risks across the two groups of workers. We disentangle the PIH effects from the precautionary motive on saving, we separate the sample of SOE workers in the 2002 survey into two groups based on their reported expectations of future income. One group expected income declines in the next five years, and the other group expected non-declines. We run IV-Tobit 2SLS regressions based on the empirical model in equation (1) for each group of the SOE workers in 2002, using all GOV workers in that year as the control group. The estimation results are reported in Table 5. The first column shows the estimation results for the group of SOE workers who expected their income to decline. The second 13 The dependent variable (W/P ) is the ratio of financial wealth to annual permanent income. A value of W/P = 0.4, for example, is equivalent to months of permanent income.

18 BREAKING THE IRON RICE BOWL AND PRECAUTIONARY SAVINGS 17 column shows the results for the group that did not expect their income to decline. In both cases, we restrict our sample to those workers whose jobs were assigned by the government to control for the self-selection bias. Our parameter of interest is again β 1, the coefficient of the SOE dummy. For the group of SOE workers who expected their income to decline, the estimated value of β 1 (Table 5, column (i)) significantly exceeds the benchmark estimate reported in Table 4, column (iii) (1.257 vs ). This finding is consistent with the PIH theory because this group of households increased their savings not just for precautionary reasons, but also for consumption smoothing. In contrast, the estimate of β 1 for those households who did not expect future income to decline (Table 5, column (ii)) is lower than the benchmark estimate (0.603 vs 0.723). The difference between the two estimates ( =0.12) is statistically significant at the 5% level. Since the PIH theory predicts that, all else equal, a household who does not expect future income to decline should save less and consume more, our estimate of β 1 = provides a lower bound of the precautionary motive for saving. We use this estimated value of β 1 to provide a lower-bound estimate of the quantitative contribution of precautionary savings to wealth accumulation, as we discuss in the next section. 14 V.4. Importance of Precautionary Saving Motive. Using the SOE reform as a natural experiment, we are able to identify the presence of precautionary saving. But an important question remains to be answered: To what extent does precautionary saving account for the observed increases in financial wealth for SOE workers between 1995 and 2002? To answer this question, we follow the literature (Carroll and Samwick, 1998; Fuchs-Schündeln and Schündeln, 2005) to quantify the contributions of precautionary saving to wealth accumulation. The idea is to compute the difference between (1) the model s predicted change in financial wealth held by SOE workers from 1995 to 2002 and (2) the counterfactual change in financial wealth had SOE workers enjoyed the same job security as GOV workers before and after the reform. To implement this idea, we go through the following steps. First, we calculate the model s predicted wealth held by SOE workers in 1995 (denote this by Ŵ soe 1995) using the benchmark estimation results reported in column (i) of Table 4, where we have corrected the self-selection bias related to occupational choice. Second, we calculate the predicted wealth held by SOE 14 Using the group of SOE workers that did not expect their income to decline in the 2002 survey might cause a downward bias in estimating precautionary saving, for two reasons. First, we do not exclude workers who expected their future income to rise; whereas for this group, the PIH channel should induce them to save less. Second, workers who expected their future income to fall might be the group who also faced higher probability of being laid-off and thus higher future income uncertainty; these workers might have stronger motives for precautionary saving than the group who expected their income not to decline.